Explainable Link Prediction for Privacy-Preserving Contact Tracing
This paper addresses the problem of user reluctance to adopt digital contact tracing applications due to privacy concerns, which is a significant barrier for public health efforts.
This paper proposes using Graph Neural Networks and explainability to improve trust and adoption of privacy-preserving digital contact tracing applications. The goal is to address user reluctance stemming from privacy concerns in existing applications.
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus. A number of digital contract tracing applications have been introduced to facilitate or complement physical contact tracing. However, there are a number of privacy issues in the implementation of contract tracing applications, which make people reluctant to install or update their infection status on these applications. In this concept paper, we present ideas from Graph Neural Networks and explainability, that could improve trust in these applications, and encourage adoption by people.